Canadian Journal of Statistics-Revue Canadienne De Statistique最新文献_第8页

A generalized single-index linear threshold model for identifying treatment-sensitive subsets based on multiple covariates and longitudinal measurements 一个广义的单指标线性阈值模型，用于识别基于多协变量和纵向测量的治疗敏感子集

IF 0.6 4区数学

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-10-17 DOI: 10.1002/cjs.11737

Xinyi Ge, Yingwei Peng, Dongsheng Tu

{"title":"A generalized single-index linear threshold model for identifying treatment-sensitive subsets based on multiple covariates and longitudinal measurements","authors":"Xinyi Ge, Yingwei Peng, Dongsheng Tu","doi":"10.1002/cjs.11737","DOIUrl":"10.1002/cjs.11737","url":null,"abstract":"Identification of a subset of patients who may be sensitive to a specific treatment is an important step towards personalized medicine. We consider the case where the effect of a treatment is assessed by longitudinal measurements, which may be continuous or categorical, such as quality of life scores assessed over the duration of a clinical trial. We assume that multiple baseline covariates, such as age and expression levels of genes, are available, and propose a generalized single-index linear threshold model to identify the treatment-sensitive subset and assess the treatment-by-subset interaction after combining these covariates. Because the model involves an indicator function with unknown parameters, conventional procedures are difficult to apply for inferences of the parameters in the model. We define smoothed generalized estimating equations and propose an inference procedure based on these equations with an efficient spectral algorithm to find their solutions. The proposed procedure is evaluated through simulation studies and an application to the analysis of data from a randomized clinical trial in advanced pancreatic cancer.","PeriodicalId":55281,"journal":{"name":"Canadian Journal of Statistics-Revue Canadienne De Statistique","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46146988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minorize–maximize algorithm for the generalized odds rate model for clustered current status data 聚类当前状态数据的广义比值率模型的Minorize–maximum算法

IF 0.6 4区数学

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-10-12 DOI: 10.1002/cjs.11733

Tong Wang, Kejun He, Wei Ma, Dipankar Bandyopadhyay, Samiran Sinha

{"title":"Minorize–maximize algorithm for the generalized odds rate model for clustered current status data","authors":"Tong Wang, Kejun He, Wei Ma, Dipankar Bandyopadhyay, Samiran Sinha","doi":"10.1002/cjs.11733","DOIUrl":"10.1002/cjs.11733","url":null,"abstract":"Current status data are widely used in epidemiology and public health, where the only observable information is the random inspection time and the event status at inspection. This article presents a unified methodology to analyze such complex data subject to clustering. Given the random clustering effect, the time to event is assumed to follow a semiparametric generalized odds rate (GOR) model. The nonparametric component of the GOR model is approximated via penalized splines, with a set of knot points that increase with the sample size. The within-subject correlation is accounted for by a random (frailty) effect. For estimation, a novel MM algorithm is developed that allows the separation of the parametric and nonparametric components of the model. This separation makes the problem conducive to applying the Newton–Raphson algorithm that quickly returns the roots. The work is accompanied by a complexity analysis of the algorithm, a rigorous asymptotic proof, and the related semiparametric efficiency of the proposed methodology. The finite sample performance of the proposed method is assessed via simulation studies. Furthermore, the proposed methodology is illustrated via real data analysis on periodontal disease studies accompanied by diagnostic checks to identify influential observations.","PeriodicalId":55281,"journal":{"name":"Canadian Journal of Statistics-Revue Canadienne De Statistique","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47713975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Causal inference for multiple treatments using fractional factorial designs 使用分数析因设计进行多重治疗的因果推断

IF 0.6 4区数学

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-10-12 DOI: 10.1002/cjs.11734

Nicole E. Pashley, Marie-Abèle C. Bind

引用次数: 6

A random walk through Canadian contributions on empirical processes and their applications in probability and statistics 随机浏览加拿大在经验过程及其在概率和统计中的应用方面的贡献

IF 0.6 4区数学

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-10-05 DOI: 10.1002/cjs.11730

Miklós Csörgő, Donald A. Dawson, Bouchra R. Nasri, Bruno N. Rémillard

引用次数: 0

The EAS approach for graphical selection consistency in vector autoregression models 向量自回归模型中图形选择一致性的EAS方法

IF 0.6 4区数学

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-09-27 DOI: 10.1002/cjs.11726

Jonathan P. Williams, Yuying Xie, Jan Hannig

{"title":"The EAS approach for graphical selection consistency in vector autoregression models","authors":"Jonathan P. Williams, Yuying Xie, Jan Hannig","doi":"10.1002/cjs.11726","DOIUrl":"10.1002/cjs.11726","url":null,"abstract":"As evidenced by various recent and significant papers within the frequentist literature, along with numerous applications in macroeconomics, genomics, and neuroscience, there continues to be substantial interest in understanding the theoretical estimation properties of high-dimensional vector autoregression (VAR) models. To date, however, while Bayesian VAR (BVAR) models have been developed and studied empirically (primarily in the econometrics literature), there exist very few theoretical investigations of the repeated-sampling properties for BVAR models in the literature, and there exist no generalized fiducial investigations of VAR models. In this direction, we construct methodology via the <math>\u0000 <mrow>\u0000 <mi>ε</mi>\u0000 </mrow></math>-admissible subsets (EAS) approach for inference based on a generalized fiducial distribution of relative model probabilities over all sets of active/inactive components (graphs) of the VAR transition matrix. We provide a mathematical proof of pairwise and strong graphical selection consistency for the EAS approach for stable VAR(1) models, and demonstrate empirically that it is an effective strategy in high-dimensional settings.","PeriodicalId":55281,"journal":{"name":"Canadian Journal of Statistics-Revue Canadienne De Statistique","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjs.11726","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43765514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Unifying genetic association tests via regression: Prospective and retrospective, parametric and nonparametric, and genotype- and allele-based tests 通过回归统一遗传关联测试:前瞻性和回顾性，参数化和非参数化，以及基于基因型和等位基因的测试

IF 0.6 4区数学

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-09-23 DOI: 10.1002/cjs.11729

Lin Zhang, Lei Sun

引用次数: 1

Efficient multiple change point detection for high-dimensional generalized linear models 高维广义线性模型的高效多变点检测

IF 0.6 4区数学

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-09-16 DOI: 10.1002/cjs.11721

Xianru Wang, Bin Liu, Xinsheng Zhang, Yufeng Liu, for the Alzheimer's Disease Neuroimaging Initiative

{"title":"Efficient multiple change point detection for high-dimensional generalized linear models","authors":"Xianru Wang, Bin Liu, Xinsheng Zhang, Yufeng Liu, for the Alzheimer's Disease Neuroimaging Initiative","doi":"10.1002/cjs.11721","DOIUrl":"10.1002/cjs.11721","url":null,"abstract":"Change point detection for high-dimensional data is an important yet challenging problem for many applications. In this article, we consider multiple change point detection in the context of high-dimensional generalized linear models, allowing the covariate dimension <math>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 </mrow></math> to grow exponentially with the sample size <math>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow></math>. The model considered is general and flexible in the sense that it covers various specific models as special cases. It can automatically account for the underlying data generation mechanism without specifying any prior knowledge about the number of change points. Based on dynamic programming and binary segmentation techniques, two algorithms are proposed to detect multiple change points, allowing the number of change points to grow with <math>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow></math>. To further improve the computational efficiency, a more efficient algorithm designed for the case of a single change point is proposed. We present theoretical properties of our proposed algorithms, including estimation consistency for the number and locations of change points as well as consistency and asymptotic distributions for the underlying regression coefficients. Finally, extensive simulation studies and application to the Alzheimer's Disease Neuroimaging Initiative data further demonstrate the competitive performance of our proposed methods.","PeriodicalId":55281,"journal":{"name":"Canadian Journal of Statistics-Revue Canadienne De Statistique","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjs.11721","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10087954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Classified generalized linear mixed model prediction incorporating pseudo-prior information 包含伪先验信息的分类广义线性混合模型预测

IF 0.6 4区数学

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-09-14 DOI: 10.1002/cjs.11727

Haiqiang Ma, Jiming Jiang

引用次数: 0

Estimation of SARS-CoV-2 antibody prevalence through serological uncertainty and daily incidence. 通过血清学不确定性和日发病率估算SARS-CoV-2抗体流行率。

IF 0.6 4区数学

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-09-01 Epub Date: 2022-08-04 DOI: 10.1002/cjs.11722

Liangliang Wang, Joosung Min, Renny Doig, Lloyd T Elliott, Caroline Colijn

{"title":"Estimation of SARS-CoV-2 antibody prevalence through serological uncertainty and daily incidence.","authors":"Liangliang Wang, Joosung Min, Renny Doig, Lloyd T Elliott, Caroline Colijn","doi":"10.1002/cjs.11722","DOIUrl":"https://doi.org/10.1002/cjs.11722","url":null,"abstract":"Serology tests for SARS-CoV-2 provide a paradigm for estimating the number of individuals who have had an infection in the past (including cases that are not detected by routine testing, which has varied over the course of the pandemic and between jurisdictions). Such estimation is challenging in cases for which we only have limited serological data and do not take into account the uncertainty of the serology test. In this work, we provide a joint Bayesian model to improve the estimation of the sero-prevalence (the proportion of the population with SARS-CoV-2 antibodies) through integrating multiple sources of data, priors on the sensitivity and specificity of the serological test, and an effective epidemiological dynamics model. We apply our model to the Greater Vancouver area, British Columbia, Canada, with data acquired during the pandemic from the end of January to May 2020. Our estimated sero-prevalence is consistent with previous literature but with a tighter credible interval.","PeriodicalId":55281,"journal":{"name":"Canadian Journal of Statistics-Revue Canadienne De Statistique","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9538003/pdf/CJS-50-734.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33515720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extended Bayesian endemic-epidemic models to incorporate mobility data into COVID-19 forecasting. 将流动性数据纳入 COVID-19 预测的扩展贝叶斯流行病学模型。

IF 0.8 4区数学

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-09-01 Epub Date: 2022-07-27 DOI: 10.1002/cjs.11723

Dirk Douwes-Schultz, Shuo Sun, Alexandra M Schmidt, Erica E M Moodie

{"title":"Extended Bayesian endemic-epidemic models to incorporate mobility data into COVID-19 forecasting.","authors":"Dirk Douwes-Schultz, Shuo Sun, Alexandra M Schmidt, Erica E M Moodie","doi":"10.1002/cjs.11723","DOIUrl":"10.1002/cjs.11723","url":null,"abstract":"Forecasting the number of daily COVID-19 cases is critical in the short-term planning of hospital and other public resources. One potentially important piece of information for forecasting COVID-19 cases is mobile device location data that measure the amount of time an individual spends at home. Endemic-epidemic (EE) time series models are recently proposed autoregressive models where the current mean case count is modelled as a weighted average of past case counts multiplied by an autoregressive rate, plus an endemic component. We extend EE models to include a distributed-lag model in order to investigate the association between mobility and the number of reported COVID-19 cases; we additionally include a weekly first-order random walk to capture additional temporal variation. Further, we introduce a shifted negative binomial weighting scheme for the past counts that is more flexible than previously proposed weighting schemes. We perform inference under a Bayesian framework to incorporate parameter uncertainty into model forecasts. We illustrate our methods using data from four US counties.","PeriodicalId":55281,"journal":{"name":"Canadian Journal of Statistics-Revue Canadienne De Statistique","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9349401/pdf/CJS-50-713.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40595647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0